We run a multi-tenant data lakehouse for higher education and public sector organizations on AWS serverless services only: S3, Glue, Athena, Lambda, Step Functions, and Iceberg tables. No cluster, no Kubernetes, and no on-call rotation. That changes what reliability means, because the signals SREs usually rely on, CPU, memory, pod health, do not exist. Our answer is that cost is the most honest signal you have. A query that returns the right answer at ten times the expected cost is a degradation, and we treat it like one. This talk walks through the SLOs we built around cost per query, file count per partition, and snapshot age, and how those SLOs drive automated remediation: compaction, snapshot expiry, catalog reconciliation, and retention enforcement that run themselves and report back. We will also cover the guardrails that keep petabyte-scale storage healthy without a human in the loop: hidden partitioning so users cannot write expensive queries by accident, and per-tenant cost limits that stop a runaway workload before it becomes a bill. You will leave with a working model for SRE on serverless data infrastructure and a checklist you can apply to any Iceberg on S3 deployment.
Ganesh Nathan is co-founder of Autolake, a serverless data lakehouse platform built entirely on AWS native services for higher education and public sector organizations. He has spent 30 years building data infrastructure across public sector and enterprise environments, and currently runs multi-tenant, petabyte-scale Iceberg on S3 deployments with no clusters and no on-call rotation. His work focuses on what reliability, governance, and cost control look like when there are no servers to watch.
